77,295 research outputs found

    Protein Bioinformatics Infrastructure for the Integration and Analysis of Multiple High-Throughput “omics” Data

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    High-throughput “omics” technologies bring new opportunities for biological and biomedical researchers to ask complex questions and gain new scientific insights. However, the voluminous, complex, and context-dependent data being maintained in heterogeneous and distributed environments plus the lack of well-defined data standard and standardized nomenclature imposes a major challenge which requires advanced computational methods and bioinformatics infrastructures for integration, mining, visualization, and comparative analysis to facilitate data-driven hypothesis generation and biological knowledge discovery. In this paper, we present the challenges in high-throughput “omics” data integration and analysis, introduce a protein-centric approach for systems integration of large and heterogeneous high-throughput “omics” data including microarray, mass spectrometry, protein sequence, protein structure, and protein interaction data, and use scientific case study to illustrate how one can use varied “omics” data from different laboratories to make useful connections that could lead to new biological knowledge

    An end-to-end software solution for the analysis of high-throughput single-cell migration data

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    The systematic study of single-cell migration requires the availability of software for assisting data inspection, quality control and analysis. This is especially important for high-throughput experiments, where multiple biological conditions are tested in parallel. Although the field of cell migration can count on different computational tools for cell segmentation and tracking, downstream data visualization, parameter extraction and statistical analysis are still left to the user and are currently not possible within a single tool. This article presents a completely new module for the open-source, cross-platform CellMissy software for cell migration data management. This module is the first tool to focus specifically on single-cell migration data downstream of image processing. It allows fast comparison across all tested conditions, providing automated data visualization, assisted data filtering and quality control, extraction of various commonly used cell migration parameters, and non-parametric statistical analysis. Importantly, the module enables parameters computation both at the trajectory-and at the step-level. Moreover, this single-cell analysis module is complemented by a new data import module that accommodates multiwell plate data obtained from high-throughput experiments, and is easily extensible through a plugin architecture. In conclusion, the end-to-end software solution presented here tackles a key bioinformatics challenge in the cell migration field, assisting researchers in their highthroughput data processing

    OmicsVolcano: software for intuitive visualization and interactive exploration of high-throughput biological data

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    Advances in omics technologies have generated exponentially larger volumes of biological data; however, their analyses and interpretation are limited to computationally proficient scientists. We created OmicsVolcano, an interactive open-source software tool to enable visualization and exploration of high-throughput biological data, while highlighting features of interest using a volcano plot interface. In contrast to existing tools, our software and user-interface design allow it to be used without requiring any programming skills to generate high-quality and presentation-ready images

    SBEAMS-Microarray: database software supporting genomic expression analyses for systems biology

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    BACKGROUND: The biological information in genomic expression data can be understood, and computationally extracted, in the context of systems of interacting molecules. The automation of this information extraction requires high throughput management and analysis of genomic expression data, and integration of these data with other data types. RESULTS: SBEAMS-Microarray, a module of the open-source Systems Biology Experiment Analysis Management System (SBEAMS), enables MIAME-compliant storage, management, analysis, and integration of high-throughput genomic expression data. It is interoperable with the Cytoscape network integration, visualization, analysis, and modeling software platform. CONCLUSION: SBEAMS-Microarray provides end-to-end support for genomic expression analyses for network-based systems biology research

    GOTree Machine (GOTM): a web-based platform for interpreting sets of interesting genes using Gene Ontology hierarchies

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    BACKGROUND: Microarray and other high-throughput technologies are producing large sets of interesting genes that are difficult to analyze directly. Bioinformatics tools are needed to interpret the functional information in the gene sets. RESULTS: We have created a web-based tool for data analysis and data visualization for sets of genes called GOTree Machine (GOTM). This tool was originally intended to analyze sets of co-regulated genes identified from microarray analysis but is adaptable for use with other gene sets from other high-throughput analyses. GOTree Machine generates a GOTree, a tree-like structure to navigate the Gene Ontology Directed Acyclic Graph for input gene sets. This system provides user friendly data navigation and visualization. Statistical analysis helps users to identify the most important Gene Ontology categories for the input gene sets and suggests biological areas that warrant further study. GOTree Machine is available online at . CONCLUSION: GOTree Machine has a broad application in functional genomic, proteomic and other high-throughput methods that generate large sets of interesting genes; its primary purpose is to help users sort for interesting patterns in gene sets

    Opportunities and challenges for digital morphology

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    Advances in digital data acquisition, analysis, and storage have revolutionized the work in many biological disciplines such as genomics, molecular phylogenetics, and structural biology, but have not yet found satisfactory acceptance in morphology. Improvements in non-invasive imaging and three-dimensional visualization techniques, however, permit high-throughput analyses also of whole biological specimens, including museum material. These developments pave the way towards a digital era in morphology. Using sea urchins (Echinodermata: Echinoidea), we provide examples illustrating the power of these techniques. However, remote visualization, the creation of a specialized database, and the implementation of standardized, world-wide accepted data deposition practices prior to publication are essential to cope with the foreseeable exponential increase in digital morphological data

    Challenges in the Multivariate Analysis of Mass Cytometry Data: The Effect of Randomization

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    Cytometry by time-of-flight (CyTOF) has emerged as a high-throughput single cell technology able to provide large samples of protein readouts. Already, there exists a large pool of advanced high-dimensional analysis algorithms that explore the observed heterogeneous distributions making intriguing biological inferences. A fact largely overlooked by these methods, however, is the effect of the established data preprocessing pipeline to the distributions of the measured quantities. In this article, we focus on randomization, a transformation used for improving data visualization, which can negatively affect multivariate data analysis methods such as dimensionality reduction, clustering, and network reconstruction algorithms. Our results indicate that randomization should be used only for visualization purposes, but not in conjunction with high-dimensional analytical tools

    Viewing the proteome: How to visualize proteomics data?

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    Proteomics has become one of the main approaches for analyzing and understanding biological systems. Yet similar to other high-throughput analysis methods, the presentation of the large amounts of obtained data in easily interpretable ways remains challenging. In this review, we present an overview of the different ways in which proteomics software supports the visualization and interpretation of proteomics data. The unique challenges and current solutions for visualizing the different aspects of proteomics data, from acquired spectra via protein identification and quantification to pathway analysis, are discussed, and examples of the most useful visualization approaches are highlighted. Finally, we offer our ideas about future directions for proteomics data visualization.acceptedVersio
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